On Tue, 18 Aug 2009, Michael Haenlein wrote:
Dear Roger,
As you suggested, I transformed my network using listw2U(...) prior to
running the errorsarlm(...) model and this time it worked fine!
I did the analysis a couple of times using different definitions of
proximity and independent variables and the results make sense overall:
Lambda: 0.84064 LR test value: 588.13 p-value: < 2.22e-16
Lambda: 0.074582 LR test value: 1075.5 p-value: < 2.22e-16
Lambda: 0.21073 LR test value: 133.26 p-value: < 2.22e-16
Lambda: 0.83978 LR test value: 586.29 p-value: < 2.22e-16
Lambda: 0.074612 LR test value: 1074.4 p-value: < 2.22e-16
Lambda: 0.20948 LR test value: 131.68 p-value: < 2.22e-16
Yet, (unfortunately) another problem came up in the meantime. I tried to
apply the same procedure to test for autocorrelation in another network I'm
working with which is substantially larger than the first one:
Number of regions: 19668
Number of nonzero links: 51598
Percentage nonzero weights: 0.01333867
Average number of links: 2.623449
Non-symmetric neighbours list
Again, I applied listw2U(...) and this worked fine resulting in:
Number of regions: 19668
Number of nonzero links: 32934
Percentage nonzero weights: 0.008513813
Average number of links: 1.674497
But when I tried to use errorsarlm(...) in the same way as before I get the
error message:
"Error in as_dsTMatrix_listw(listw) : ncard2 incorrectly given"
As I said, the approach I took is exactly the same. Both networks are
sampled out of a much larger network, it's just that the second network uses
a different sampling approach and has more regions. So I assume that the
error message is due to the larger network size.
Is there any chance to work around this problem?
Should I probably use "spam" instead of "Matrix"?
Or is errorsarlm(...) simply just not suited to analyze networks of such
size?
Dear Michael,
There is another implementation of errorsarlm(), called spautolm(). Could
you try that? "Matrix" is prefered to "spam", and ongoing development is
with the authors of the now recommended Matrix package. In spautolm(),
some tricks that they put together are used. If you still run into
problems, I'd be interested in trying to run the analyses with your data
and weights, to see what may need fixing, as handling larger data sets is
not fully settled at all, very much still work in progress.
We know how to do it in theory, and the results are OK, but there are
wrinkles that need navigating. When the number of observations gets very
large, there is also the issue of ensuring homogeneity (so spautolm() has
a weights= argument; it was written to match the development of spatial
regression in Waller & Gotway (2004)).
Best wishes,
Roger
Thanks very much for your help,
Best,
Michael
-----Original Message-----
From: Roger Bivand [mailto:roger.biv...@nhh.no]
Sent: Montag, 17. August 2009 18:27
To: Michael Haenlein
Cc: r-sig-geo@stat.math.ethz.ch
Subject: RE: [R-sig-Geo] Question spdep package - Moran's I
The message from the Matrix method was correct, spam ought not to have tried
to continue, I guess it is failing and lambda is ending up at the end of its
interval.
The spatial weights must be symmetric or similar to symmetric for spatial
regression fitting. The Jacobian is found by a sparse Cholesky
decomposition, which means that W must be symmetric. listw2U() should get
you there, I should have spotted the problem before. You may also need to
play with the interval= argument.
Let me know how it goes.
Roger
--
Roger Bivand
Economic Geography Section, Department of Economics, Norwegian School of
Economics and Business Administration, Helleveien 30, N-5045 Bergen,
Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
e-mail: roger.biv...@nhh.no
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